Distributed processing of large graphs in python
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Transcript of Distributed processing of large graphs in python
• Mentors are world-class. CTOs, library authors, inventors, founders of fast-growing companies, etc
• DSR accepts fewer than 5% of the applications
• Strong focus on commercial awareness
• 5 years of working experience on average
• 30+ partner companies in Europe
Why is DSR talking about Scala/Spark?
They are behind Scala
IBM is behind this They hired us to make training materials
What is a good question?• Business case • Data available • Technology to answer the question is available • We know when the solution worked
What is a good question?• Business case • Data available • Technology to answer the question is available • We know when the solution worked
The question: When should I tweet to influence the right account?
Or ‘beat Buffer at their own game’
What is a good question?• Business case • Data available • Technology to answer the question is available
What is a good question?• Business case • Data available • Technology to answer the question is available • We know when the solution worked
Sampling in NetworksNote that sampling in Networks is fraught with difficulties. One cannot simply sample the edges and nodes and expect that the sample be representative of the original network. In the graph below, a sample that missed node 1 or 2 would disconnect the two clusters, and would not have the same properties as the original
Node 11Node 2
Random surfer
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Visited more often: • Nodes with many links • Coming from frequently visited nodes
Teleport
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At regular node: invoke teleport operation with probability α and standard random walk with probability (1-α)
(1-α)
α
Personalized pagerank
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At regular node: invoke teleport operation with probability α and standard random walk with probability (1-α). When teleporting, go to target node
(1-α)
Personalized pagerank
A
B
C
D
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At regular node: invoke teleport operation with probability α and standard random walk with probability (1-α). When teleporting, go to target node
(1-α)α
Personalized pagerank
• Special case of Pagerank with priors (distribution of weights over the nodes)
A partitioned, distributed graph processing engine is significantly more complex and difficult to build
GraphX and graphframes (new in spark 2.0)• GraphX is to RDD as graphframe is to dataframe • GraphX is lower level, and the API is scala-only. Graphframe is
very new:
• It’s not designed to be a graph database, as neo4J. Nodes and edges can contain metadata, but the query engine is not as complete as cypher
Advantages of graphframes• Graphframes have a python API
• Graphframes give you simple querying for free. GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries
• They contain most of the algorithms in graphX, but the API is less well-tested
• Pyspark shell instead of spark-shell
Distributed PageRank• Problem: Computing PageRank on graph too large for one
machine • Algorithm: – Shard edges randomly, – compute on each machine – average results • Basic idea: Duplicate edges from low-degree nodes. Gives an
unbiased estimator
Summary of implementation, benefits• Graph theory is a really flexible way to represent a problem
• Data structures to represent graphs are mature
• You can do now out-of-core, distributed graph analysis for cheap
• Implementations are there for even state-of-the-art methods
Summary, finding a problem• We live in an age of abundance (methods, data, hardware, ideas)
• Finding the question is more than half of the battle
• I had about a week to prepare this talk, but I managed to put together something that showcases what you can do with large graphs today, and it could be effective as a startup idea
• My question is not great because you cannot demonstrate that it works till you use it (common problem for unsupervised methods)
The question: When should I tweet to influence the right account?
Or ‘beat Buffer at their own game’
References: Drawing graphs• Graphs in this slide set have been drawn with Gephi • If you use Zeppelin notebook, you can draw graphs with:
drawGraph(org.apache.spark.graphx.util. GraphGenerators.rmatGraph(sc,32,60))
25 videos explaining ML on spark, 50 more to come. A bunch on graphX• For people who already know ML
• http://datascienceretreat.com/videos/data-science-with-scala-and-spark
About learning new tech over seven weekends• You have time and enjoy using it to learn alone: learn it ‘the
hard way’
• You are extremely motivated and talented, have money: Apply for DSR
• You want your weekends for yourself. You are already very good but want to switch jobs. Apply for codekitt
Thanks!Jose Quesada Director, Data Science Retreat [email protected] @quesada
http://datascienceretreat.com/
codekitt.com